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@ -365,7 +365,6 @@ class DistributeTranspiler:
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else:
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self._append_pserver_non_opt_ops(optimize_sub_program, opt_op)
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print("####", optimize_sub_program)
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pserver_program.global_block().append_op(
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type="recv",
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inputs={"RX": self.param_grad_ep_mapping[endpoint]["grads"]
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@ -407,7 +406,6 @@ class DistributeTranspiler:
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# 1. create vars
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created_var_map = dict()
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for var in params:
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print("%%%% append var", var.name, var.shape)
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tmpvar = s_prog.global_block().create_var(
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name=var.name,
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persistable=True,
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@ -430,6 +428,8 @@ class DistributeTranspiler:
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if var.name in created_var_map:
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var_on_pserver = True
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if var_on_pserver:
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# gaussian_random use attr to determine tensor shape
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op.attrs["shape"] = new_outputs["Out"].shape
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s_prog.global_block().append_op(
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type=op.type,
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inputs=op.inputs,
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